{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Amortization Example in Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code to support article at [Practical Business Python](http://pbpython.com/amortization-model.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup the imports and matplotlib plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import date\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function to build an amortization table/schedule and return the value as a pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def amortization_table(interest_rate, years, payments_year, principal, addl_principal=0, start_date=date.today()):\n",
    "    \"\"\" Calculate the amortization schedule given the loan details\n",
    "    \n",
    "     Args:\n",
    "        interest_rate: The annual interest rate for this loan\n",
    "        years: Number of years for the loan\n",
    "        payments_year: Number of payments in a year\n",
    "        principal: Amount borrowed\n",
    "        addl_principal (optional): Additional payments to be made each period. Assume 0 if nothing provided.\n",
    "                                   must be a value less then 0, the function will convert a positive value to\n",
    "                                   negative\n",
    "        start_date (optional): Start date. Will start on first of next month if none provided\n",
    "\n",
    "    Returns:\n",
    "        schedule: Amortization schedule as a pandas dataframe\n",
    "        summary: Pandas dataframe that summarizes the payoff information\n",
    "    \"\"\"\n",
    "    # Ensure the additional payments are negative\n",
    "    if addl_principal > 0:\n",
    "        addl_principal = -addl_principal\n",
    "    \n",
    "    # Create an index of the payment dates\n",
    "    rng = pd.date_range(start_date, periods=years * payments_year, freq='MS')\n",
    "    rng.name = \"Payment_Date\"\n",
    "    \n",
    "    # Build up the Amortization schedule as a DataFrame\n",
    "    df = pd.DataFrame(index=rng,columns=['Payment', 'Principal', 'Interest', \n",
    "                                         'Addl_Principal', 'Curr_Balance'], dtype='float')\n",
    "    \n",
    "    # Add index by period (start at 1 not 0)\n",
    "    df.reset_index(inplace=True)\n",
    "    df.index += 1\n",
    "    df.index.name = \"Period\"\n",
    "    \n",
    "    # Calculate the payment, principal and interests amounts using built in Numpy functions\n",
    "    per_payment = np.pmt(interest_rate/payments_year, years*payments_year, principal)\n",
    "    df[\"Payment\"] = per_payment\n",
    "    df[\"Principal\"] = np.ppmt(interest_rate/payments_year, df.index, years*payments_year, principal)\n",
    "    df[\"Interest\"] = np.ipmt(interest_rate/payments_year, df.index, years*payments_year, principal)\n",
    "        \n",
    "    # Round the values\n",
    "    df = df.round(2) \n",
    "    \n",
    "    # Add in the additional principal payments\n",
    "    df[\"Addl_Principal\"] = addl_principal\n",
    "    \n",
    "    # Store the Cumulative Principal Payments and ensure it never gets larger than the original principal\n",
    "    df[\"Cumulative_Principal\"] = (df[\"Principal\"] + df[\"Addl_Principal\"]).cumsum()\n",
    "    df[\"Cumulative_Principal\"] = df[\"Cumulative_Principal\"].clip(lower=-principal)\n",
    "    \n",
    "    # Calculate the current balance for each period\n",
    "    df[\"Curr_Balance\"] = principal + df[\"Cumulative_Principal\"]\n",
    "    \n",
    "    # Determine the last payment date\n",
    "    try:\n",
    "        last_payment = df.query(\"Curr_Balance <= 0\")[\"Curr_Balance\"].idxmax(axis=1, skipna=True)\n",
    "    except ValueError:\n",
    "        last_payment = df.last_valid_index()\n",
    "    \n",
    "    last_payment_date = \"{:%m-%d-%Y}\".format(df.loc[last_payment, \"Payment_Date\"])\n",
    "        \n",
    "    # Truncate the data frame if we have additional principal payments:\n",
    "    if addl_principal != 0:\n",
    "                \n",
    "        # Remove the extra payment periods\n",
    "        df = df.loc[0:last_payment].copy()\n",
    "        \n",
    "        # Calculate the principal for the last row\n",
    "        df.loc[last_payment, \"Principal\"] = -(df.loc[last_payment-1, \"Curr_Balance\"])\n",
    "        \n",
    "        # Calculate the total payment for the last row\n",
    "        df.loc[last_payment, \"Payment\"] = df.loc[last_payment, [\"Principal\", \"Interest\"]].sum()\n",
    "        \n",
    "        # Zero out the additional principal\n",
    "        df.loc[last_payment, \"Addl_Principal\"] = 0\n",
    "        \n",
    "    # Get the payment info into a DataFrame in column order\n",
    "    payment_info = (df[[\"Payment\", \"Principal\", \"Addl_Principal\", \"Interest\"]]\n",
    "                    .sum().to_frame().T)\n",
    "       \n",
    "    # Format the Date DataFrame\n",
    "    payment_details = pd.DataFrame.from_dict(dict([('payoff_date', [last_payment_date]),\n",
    "                                               ('Interest Rate', [interest_rate]),\n",
    "                                               ('Number of years', [years])\n",
    "                                              ]))\n",
    "    # Add a column showing how much we pay each period.\n",
    "    # Combine addl principal with principal for total payment\n",
    "    payment_details[\"Period_Payment\"] = round(per_payment, 2) + addl_principal\n",
    "    \n",
    "    payment_summary = pd.concat([payment_details, payment_info], axis=1)\n",
    "    return df, payment_summary\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples of running the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedule1, stats1 = amortization_table(0.05, 30, 12, 100000, addl_principal=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at the start and end of the table as well as the summary stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Payment_Date</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Principal</th>\n",
       "      <th>Interest</th>\n",
       "      <th>Addl_Principal</th>\n",
       "      <th>Curr_Balance</th>\n",
       "      <th>Cumulative_Principal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-120.15</td>\n",
       "      <td>-416.67</td>\n",
       "      <td>0</td>\n",
       "      <td>99879.85</td>\n",
       "      <td>-120.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-120.66</td>\n",
       "      <td>-416.17</td>\n",
       "      <td>0</td>\n",
       "      <td>99759.19</td>\n",
       "      <td>-240.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-121.16</td>\n",
       "      <td>-415.66</td>\n",
       "      <td>0</td>\n",
       "      <td>99638.03</td>\n",
       "      <td>-361.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-121.66</td>\n",
       "      <td>-415.16</td>\n",
       "      <td>0</td>\n",
       "      <td>99516.37</td>\n",
       "      <td>-483.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-122.17</td>\n",
       "      <td>-414.65</td>\n",
       "      <td>0</td>\n",
       "      <td>99394.20</td>\n",
       "      <td>-605.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Payment_Date  Payment  Principal  Interest  Addl_Principal  \\\n",
       "Period                                                              \n",
       "1        2020-01-01  -536.82    -120.15   -416.67               0   \n",
       "2        2020-02-01  -536.82    -120.66   -416.17               0   \n",
       "3        2020-03-01  -536.82    -121.16   -415.66               0   \n",
       "4        2020-04-01  -536.82    -121.66   -415.16               0   \n",
       "5        2020-05-01  -536.82    -122.17   -414.65               0   \n",
       "\n",
       "        Curr_Balance  Cumulative_Principal  \n",
       "Period                                      \n",
       "1           99879.85               -120.15  \n",
       "2           99759.19               -240.81  \n",
       "3           99638.03               -361.97  \n",
       "4           99516.37               -483.63  \n",
       "5           99394.20               -605.80  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Payment_Date</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Principal</th>\n",
       "      <th>Interest</th>\n",
       "      <th>Addl_Principal</th>\n",
       "      <th>Curr_Balance</th>\n",
       "      <th>Cumulative_Principal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>2049-08-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-525.78</td>\n",
       "      <td>-11.05</td>\n",
       "      <td>0</td>\n",
       "      <td>2125.09</td>\n",
       "      <td>-97874.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>2049-09-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-527.97</td>\n",
       "      <td>-8.85</td>\n",
       "      <td>0</td>\n",
       "      <td>1597.12</td>\n",
       "      <td>-98402.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>2049-10-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-530.17</td>\n",
       "      <td>-6.65</td>\n",
       "      <td>0</td>\n",
       "      <td>1066.95</td>\n",
       "      <td>-98933.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>2049-11-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-532.38</td>\n",
       "      <td>-4.45</td>\n",
       "      <td>0</td>\n",
       "      <td>534.57</td>\n",
       "      <td>-99465.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>2049-12-01</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-534.59</td>\n",
       "      <td>-2.23</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-100000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Payment_Date  Payment  Principal  Interest  Addl_Principal  \\\n",
       "Period                                                              \n",
       "356      2049-08-01  -536.82    -525.78    -11.05               0   \n",
       "357      2049-09-01  -536.82    -527.97     -8.85               0   \n",
       "358      2049-10-01  -536.82    -530.17     -6.65               0   \n",
       "359      2049-11-01  -536.82    -532.38     -4.45               0   \n",
       "360      2049-12-01  -536.82    -534.59     -2.23               0   \n",
       "\n",
       "        Curr_Balance  Cumulative_Principal  \n",
       "Period                                      \n",
       "356          2125.09             -97874.91  \n",
       "357          1597.12             -98402.88  \n",
       "358          1066.95             -98933.05  \n",
       "359           534.57             -99465.43  \n",
       "360             0.00            -100000.00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>payoff_date</th>\n",
       "      <th>Interest Rate</th>\n",
       "      <th>Number of years</th>\n",
       "      <th>Period_Payment</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Principal</th>\n",
       "      <th>Addl_Principal</th>\n",
       "      <th>Interest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12-01-2049</td>\n",
       "      <td>0.05</td>\n",
       "      <td>30</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-193255.2</td>\n",
       "      <td>-100000.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-93255.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  payoff_date  Interest Rate  Number of years  Period_Payment   Payment  \\\n",
       "0  12-01-2049           0.05               30         -536.82 -193255.2   \n",
       "\n",
       "   Principal  Addl_Principal  Interest  \n",
       "0 -100000.02             0.0 -93255.69  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try running some other scenarios and combining them into a single DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedule2, stats2 = amortization_table(0.05, 30, 12, 100000, addl_principal=-200)\n",
    "schedule3, stats3 = amortization_table(0.04, 15, 12, 100000, addl_principal=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>payoff_date</th>\n",
       "      <th>Interest Rate</th>\n",
       "      <th>Number of years</th>\n",
       "      <th>Period_Payment</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Principal</th>\n",
       "      <th>Addl_Principal</th>\n",
       "      <th>Interest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12-01-2049</td>\n",
       "      <td>0.05</td>\n",
       "      <td>30</td>\n",
       "      <td>-536.82</td>\n",
       "      <td>-193255.20</td>\n",
       "      <td>-100000.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-93255.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05-01-2040</td>\n",
       "      <td>0.05</td>\n",
       "      <td>30</td>\n",
       "      <td>-736.82</td>\n",
       "      <td>-131689.78</td>\n",
       "      <td>-51200.00</td>\n",
       "      <td>-48800.0</td>\n",
       "      <td>-80490.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12-01-2034</td>\n",
       "      <td>0.04</td>\n",
       "      <td>15</td>\n",
       "      <td>-739.69</td>\n",
       "      <td>-133144.20</td>\n",
       "      <td>-100000.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-33143.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  payoff_date  Interest Rate  Number of years  Period_Payment    Payment  \\\n",
       "0  12-01-2049           0.05               30         -536.82 -193255.20   \n",
       "1  05-01-2040           0.05               30         -736.82 -131689.78   \n",
       "2  12-01-2034           0.04               15         -739.69 -133144.20   \n",
       "\n",
       "   Principal  Addl_Principal  Interest  \n",
       "0 -100000.02             0.0 -93255.69  \n",
       "1  -51200.00        -48800.0 -80490.16  \n",
       "2 -100000.03             0.0 -33143.85  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine all the scenarios into 1 view\n",
    "pd.concat([stats1, stats2, stats3], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Payment_Date</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Principal</th>\n",
       "      <th>Interest</th>\n",
       "      <th>Addl_Principal</th>\n",
       "      <th>Curr_Balance</th>\n",
       "      <th>Cumulative_Principal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>-739.69</td>\n",
       "      <td>-406.35</td>\n",
       "      <td>-333.33</td>\n",
       "      <td>0</td>\n",
       "      <td>99593.65</td>\n",
       "      <td>-406.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>-739.69</td>\n",
       "      <td>-407.71</td>\n",
       "      <td>-331.98</td>\n",
       "      <td>0</td>\n",
       "      <td>99185.94</td>\n",
       "      <td>-814.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>-739.69</td>\n",
       "      <td>-409.07</td>\n",
       "      <td>-330.62</td>\n",
       "      <td>0</td>\n",
       "      <td>98776.87</td>\n",
       "      <td>-1223.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>-739.69</td>\n",
       "      <td>-410.43</td>\n",
       "      <td>-329.26</td>\n",
       "      <td>0</td>\n",
       "      <td>98366.44</td>\n",
       "      <td>-1633.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>-739.69</td>\n",
       "      <td>-411.80</td>\n",
       "      <td>-327.89</td>\n",
       "      <td>0</td>\n",
       "      <td>97954.64</td>\n",
       "      <td>-2045.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Payment_Date  Payment  Principal  Interest  Addl_Principal  \\\n",
       "Period                                                              \n",
       "1        2020-01-01  -739.69    -406.35   -333.33               0   \n",
       "2        2020-02-01  -739.69    -407.71   -331.98               0   \n",
       "3        2020-03-01  -739.69    -409.07   -330.62               0   \n",
       "4        2020-04-01  -739.69    -410.43   -329.26               0   \n",
       "5        2020-05-01  -739.69    -411.80   -327.89               0   \n",
       "\n",
       "        Curr_Balance  Cumulative_Principal  \n",
       "Period                                      \n",
       "1           99593.65               -406.35  \n",
       "2           99185.94               -814.06  \n",
       "3           98776.87              -1223.13  \n",
       "4           98366.44              -1633.56  \n",
       "5           97954.64              -2045.36  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedule3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples of plotting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "schedule1.plot(x='Payment_Date', y='Curr_Balance', title=\"Pay Off Timeline\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "schedule1.plot(x='Payment_Date', y='Curr_Balance', label=\"Scenario 1\", ax=ax)\n",
    "schedule2.plot(x='Payment_Date', y='Curr_Balance', label=\"Scenario 2\", ax=ax)\n",
    "schedule3.plot(x='Payment_Date', y='Curr_Balance', label=\"Scenario 3\", ax=ax)\n",
    "plt.title(\"Pay Off Timelines\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "schedule1[\"Cum_Interest\"] = schedule1[\"Interest\"].abs().cumsum()\n",
    "schedule2[\"Cum_Interest\"] = schedule2[\"Interest\"].abs().cumsum()\n",
    "schedule3[\"Cum_Interest\"] = schedule3[\"Interest\"].abs().cumsum()\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "\n",
    "schedule1.plot(x='Payment_Date', y='Cum_Interest', label=\"Scenario 1\", ax=ax)\n",
    "schedule2.plot(x='Payment_Date', y='Cum_Interest', label=\"Scenario 2\", ax=ax, style='+')\n",
    "schedule3.plot(x='Payment_Date', y='Cum_Interest', label=\"Scenario 3\", ax=ax)\n",
    "\n",
    "ax.legend(loc=\"best\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kSZOwfft27Ny5E/n5+UhNTYWXlxcAIDMzE7t27cLFixcxbdo0pKSkAABmz54tHRo5b948PPjgg2hvb0doaCjmzJmDoqIirFixAh9//DHy8/Ph4OCASZMmYcmSJWb3XVZWhj179mDbtm3Su6XBgwcjJSUFq1evxieffIKpU6f26nlwcHBAS0sL2trapLF169ZJPbg/9Nj0hwwZgnXr1lmMu7u7Y+nSpVaXSUhIQEJCgsV4UFAQ0tPTr6DMvut6e1p1yVhPbzGJ6Orp6OjAlClTcObMGcyZMwf/8R//AaBz9+6QIUMAdPajS3cTX2rFihWYNm0aRo4ciRkzZgDo/OL5n376Cf/85z8hhMCsWbNQUlKCqKgovPLKK9BqtWhubsbUqVNxxx13wMvLCw0NDQgLC0NaWhpqampQUFCAvXv3QqVSob6+3uJ+t2/fjscffxwqlQqLFy/GN998g7i4ODQ1NeGxxx7DokWLrDb9M2fO4LbbboOHhwfS0tIQFRWFSZMmIScnB9OmTcOjjz6K3bt3Y+zYsfD19e2vp9k+LrhGRNc+jUaDzz77DPX19UhKSsK3336LG264odfL+/n5Ydy4cWafBxYWFiI/Px/79+8HAFy4cAHl5eWIiopCdnY2Pv30UwBAZWUlzp49Cy8vLwwaNEhq0l5eXlCr1XjmmWcwefJks7m7nDhxAk899RQ+/vhjuLq64uOPP8aGDRtQWVkpHQDzS0OHDsX+/fuh1Wpx+PBhzJs3D4WFhRg8eDBef/11ODg44MKFC3jooYfw1ltvYenSpaisrMR9991ntYa+4GUYiEhWPD09ERMTg88//xxA5wfNVVWd79mrqqrMDv3+JbVaLe1O7pKSkiLtO9+3bx/uu+8+FBUV4euvv8ZHH32EvLw8hIaGSmfAOjs7S7uWHB0dsXv3btx+++3YvXs3HnnkEYv7FEJArVajrKwMEydOBNC5uwkAmpub4ezsbLGMs7OzdMh7REQErrvuOpw+fdos85e//AUPPPAAvv76awwePBgbN25ERkZGT09fj9j0ieiqq6urk7aIm5ub8cUXXyAoKAgAcNttt2Hnzp0AgJ07d+L222/v9byxsbHYtm0bLly4AKDzMu0GgwGNjY3w8vKCi4sLTp48iW+++cbq8k1NTWhqasKUKVOwfPlyHDt2zCJz/fXX49ChQwgKCkJRURGAzncYQghkZWXhrrvusvp4uz4kPn36NH744QcMGzZM+rnBYEBRURGmT5+O5uZm6YWsL5d96A5378D88LSuzwC4/5+UrLvf/74c0tiXfFVVFVJSUtDR0QGTyYS77roLU6ZMAdB57a8FCxbg3XffRUBAADZt2tTr+588eTJOnz4tNd7BgwcjKysLkydPxjvvvIO4uDgEBwd3e1BJQ0MD5s2bh4sXL0IIYfEBMgDce++9SEtLw/vvv4+ioiJMnToVcXFxOH78OEJDQzFz5kyLZfbt24eMjAzpcgtr1641u8zNunXr8OSTT0KlUuE///M/sXXrVsTFxWH27Nm9fuzdYdMnoqtuzJgxyM/Pt/oCodPpsGPHjl7N88orr1iMLViwAMnJyRbj27ZtsxhzcHDAiRMnpNv+/v7YtWvXZe8zNDQUt99+O/74xz9i+fLl8Pf3R3NzM0aMGIGYmBirZ8vefffduPvu7s+FWL16tfRcuLi44L333rtsDX3Bpk9E9Cs9+uij+Oyzz/D000+jrq4O7u7umD59unTUkZyw6feRtV1BAHcHESndlClTpF1ScsYPcomoT9fGIvnpy/pj0yciqNXqPn1AS/LR1tZmcZjq5XD3DhHB2dkZLS0tuHjx4mUv0+vk5NSnwwb7kr+Ws7aaWwgBNzc3q+cCdIdN34a4/5/shUql6tU3L/HqpFeWteXc3t7efaqDu3eIiBSETZ+ISEHY9ImIFIT79GWC+/+JaCBwS5+ISEHY9ImIFIRNn4hIQbhP3w5x/z8RXSlu6RMRKQibPhGRgrDpExEpCPfpK0DXZwDc/09E3NInIlIQNn0iIgVh0yciUhDu0ycz1s4B4P5/omsHt/SJiBSETZ+ISEHY9ImIFIRNn4hIQfhBLl0xXviNyP5wS5+ISEF6vaVvMpmwePFi6HQ6LF68GE1NTcjIyEBNTQ18fHyQkpICNzc3AEBOTg4KCgqgVquRmJiI8PBwAEB5eTmysrLQ2tqKiIgIJCYmQqVS2eaRERGRhV5v6e/evRsBAQHS7dzcXISFhSEzMxNhYWHIzc0FAFRUVKC4uBjr16/HkiVLsHnzZphMJgBAdnY25s+fj8zMTJw7dw6lpaX9/HCIiOhyetX06+rqcOjQIUyePFkaKykpQWxsLAAgNjYWJSUl0nhMTAwcHR3h6+sLPz8/lJWVwWg0orm5GSEhIVCpVJgwYYK0DBERDYxe7d5566238PDDD6O5uVkaq6+vh1arBQBotVo0NDQAAAwGA0aPHi3ldDodDAYDNBoNvL29pXFvb28YDIZ+eRAkf/zQl0geemz6Bw8ehKenJ0aNGoV//etfPU4ohOjTuDV5eXnIy8sDAKxZswZ6vd6sUVxKr9dbHbeWt1W2u7wcst3l5fxcXMrBwaHHjK2zcqnD3rJyqUMOWVnV0VPg5MmTOHDgAA4fPozW1lY0NzcjMzMTnp6eMBqN0Gq1MBqN8PDwANC5BV9XVyctbzAYoNPpLMbr6uqg0+ms3mdcXBzi4uKk27W1td3Wd7mfDVRWLnXIIdvfc+v1+l7PZ6usXOqwt6xc6pBD9mrU4e/vbzXf4z79Bx98EBs3bkRWVhaefPJJ3HTTTXj88ccRGRmJwsJCAEBhYSGioqIAAJGRkSguLkZbWxuqq6tRWVmJ4OBgaLVauLi44NSpUxBCoKioCJGRkb16UERE1D+u+OSs+Ph4ZGRkoKCgAHq9HqmpqQCAwMBAREdHIzU1FWq1GklJSVCrO19bkpOTsWHDBrS2tiI8PBwRERH98yiIiKhX+tT0b7zxRtx4440AAHd3dyxdutRqLiEhAQkJCRbjQUFBSE9Pv4IySUn4oS+R7fCMXCIiBWHTJyJSEDZ9IiIFYdMnIlIQNn0iIgXh9fTJrvFIH6K+4ZY+EZGCsOkTESkImz4RkYKw6RMRKQg/yCVF6frglx/6klJxS5+ISEHY9ImIFIRNn4hIQdj0iYgUhE2fiEhBePQOUTesXeKBR/qQveOWPhGRgrDpExEpCJs+EZGCsOkTESkImz4RkYKw6RMRKQgP2STqBzy8k+wFt/SJiBSETZ+ISEHY9ImIFIRNn4hIQdj0iYgUhEfvEA0wa0f6ADzahwYGt/SJiBSETZ+ISEHY9ImIFKTHffqtra1YtmwZ2tvb0dHRgVtuuQX33nsvmpqakJGRgZqaGvj4+CAlJQVubm4AgJycHBQUFECtViMxMRHh4eEAgPLycmRlZaG1tRURERFITEyESqWy7SMkIiJJj1v6jo6OWLZsGdatW4e1a9eitLQUp06dQm5uLsLCwpCZmYmwsDDk5uYCACoqKlBcXIz169djyZIl2Lx5M0wmEwAgOzsb8+fPR2ZmJs6dO4fS0lLbPjoiIjLTY9NXqVRwdnYGAHR0dKCjowMqlQolJSWIjY0FAMTGxqKkpAQAUFJSgpiYGDg6OsLX1xd+fn4oKyuD0WhEc3MzQkJCoFKpMGHCBGkZIiIaGL06ZNNkMiEtLQ3nzp3D7bffjtGjR6O+vh5arRYAoNVq0dDQAAAwGAwYPXq0tKxOp4PBYIBGo4G3t7c07u3tDYPBYPX+8vLykJeXBwBYs2YN9Hq92aFtl9Lr9VbHreVtle0uL4dsd3k+F/LKdpevmh5jdbkhOcXdzNLJwcGh2/sfqKxc6pBDVlZ19CakVquxbt06/Pzzz3j55Zfxww8/dJsVQvRp3Jq4uDjExcVJt2tra7vNXu5nA5WVSx1yyMqlDnvL9vfcer2+1/PZKiuXOuSQvRp1+Pv7W8336eidwYMHY8yYMSgtLYWnpyeMRiMAwGg0wsPDA0DnFnxdXZ20jMFggE6nsxivq6uDTqfry90TEdGv1GPTb2howM8//wyg80ieo0ePIiAgAJGRkSgsLAQAFBYWIioqCgAQGRmJ4uJitLW1obq6GpWVlQgODoZWq4WLiwtOnToFIQSKiooQGRlpw4dGRES/1OPuHaPRiKysLJhMJgghEB0djbFjxyIkJAQZGRkoKCiAXq9HamoqACAwMBDR0dFITU2FWq1GUlIS1OrO15bk5GRs2LABra2tCA8PR0REhG0fHRERmemx6Q8fPhxr1661GHd3d8fSpUutLpOQkICEhASL8aCgIKSnp19BmURE1B94Ri4RkYKw6RMRKQgvrUx0jeAlm6k3uKVPRKQgbPpERArCpk9EpCBs+kRECsKmT0SkIGz6REQKwkM2iRSq6xBPHt6pLNzSJyJSEDZ9IiIFYdMnIlIQNn0iIgVh0yciUhA2fSIiBWHTJyJSEDZ9IiIF4clZRNQjnsh17eCWPhGRgrDpExEpCJs+EZGCsOkTESkImz4RkYKw6RMRKQibPhGRgvA4fSLqV13H9AP/Pq6fx/TLB7f0iYgUhE2fiEhB2PSJiBSETZ+ISEHY9ImIFKTHo3dqa2uRlZWF8+fPQ6VSIS4uDnfccQeampqQkZGBmpoa+Pj4ICUlBW5ubgCAnJwcFBQUQK1WIzExEeHh4QCA8vJyZGVlobW1FREREUhMTIRKpbLtIyQiIkmPW/oajQazZs1CRkYGVq1ahT179qCiogK5ubkICwtDZmYmwsLCkJubCwCoqKhAcXEx1q9fjyVLlmDz5s0wmUwAgOzsbMyfPx+ZmZk4d+4cSktLbfvoiIjITI9NX6vVYtSoUQAAFxcXBAQEwGAwoKSkBLGxsQCA2NhYlJSUAABKSkoQExMDR0dH+Pr6ws/PD2VlZTAajWhubkZISAhUKhUmTJggLUNERAOjTydnVVdX4/Tp0wgODkZ9fT20Wi2AzheGhoYGAIDBYMDo0aOlZXQ6HQwGAzQaDby9vaVxb29vGAyG/ngMRGSnrJ3IBfBkLlvqddNvaWlBeno65syZA1dX125zQog+jVuTl5eHvLw8AMCaNWug1+vNfiEupdfrrY5by9sq211eDtnu8nwu5JXtLs/nwjoHB4ceM3LKyqqO3oTa29uRnp6OW2+9Fb/73e8AAJ6enjAajdBqtTAajfDw8ADQuQVfV1cnLWswGKDT6SzG6+rqoNPprN5fXFwc4uLipNu1tbXd1na5nw1UVi51yCErlzrsLSuXOuSQ7U1er9f3ek45ZK9GHf7+/lbzPe7TF0Jg48aNCAgIwJ133imNR0ZGorCwEABQWFiIqKgoaby4uBhtbW2orq5GZWUlgoODodVq4eLiglOnTkEIgaKiIkRGRvbqQRERUf/ocUv/5MmTKCoqwrBhw/DMM88AAB544AHEx8cjIyMDBQUF0Ov1SE1NBQAEBgYiOjoaqampUKvVSEpKglrd+dqSnJyMDRs2oLW1FeHh4YiIiLDhQyMiol/qsenfcMMN2LFjh9WfLV261Op4QkICEhISLMaDgoKQnp7exxKJiKi/8IxcIiIFYdMnIlIQNn0iIgVh0yciUhB+XSIR2QWevds/uKVPRKQgbPpERArCpk9EpCBs+kRECsKmT0SkIGz6REQKwqZPRKQgbPpERArCk7OI6JrDE7m6xy19IiIFYdMnIlIQNn0iIgVh0yciUhA2fSIiBWHTJyJSEDZ9IiIFYdMnIlIQNn0iIgVh0yciUhBehoGIFE1pl2zglj4RkYKw6RMRKQibPhGRgrDpExEpCJs+EZGCsOkTESkImz4RkYKw6RMRKUiPJ2dt2LABhw4dgqenJ9LT0wEATU1NyMjIQE1NDXx8fJCSkgI3NzcAQE5ODgoKCqBWq5GYmIjw8HAAQHl5ObKystDa2oqIiAgkJiZCpVLZ8KEREdEv9bilP3HiRDz33HNmY7m5uQgLC0NmZibCwsKQm5sLAKioqEBxcTHWr1+PJUuWYPPmzTCZTACA7OxszJ8/H5mZmTh37hxKS0tt8HCIiOhyemz6Y8aMkbbiu5SUlCA2NhYAEBsbi5KSEmk8JiYGjo6O8PX1hZ+fH8rKymA0GtHc3IyQkIOpdp8AABDKSURBVBCoVCpMmDBBWoaIyJ50zLsbHfPuRtX0GOnf9uSK9unX19dDq9UCALRaLRoaGgAABoMB3t7eUk6n08FgMFiMe3t7w2Aw/Jq6iYjoCvTrBdeEEH0a705eXh7y8vIAAGvWrIFerze7ENKl9Hq91XFreVtlu8vLIdtdns+FvLLd5flcyCvbXb677KUcHBx6lbNlFrjCpu/p6Qmj0QitVguj0QgPDw8AnVvwdXV1Us5gMECn01mM19XVQafTdTt/XFwc4uLipNu1tbXdZi/3s4HKyqUOOWTlUoe9ZeVShxyycqmjv7N6vb7Xc/ZH1t/f32r+inbvREZGorCwEABQWFiIqKgoaby4uBhtbW2orq5GZWUlgoODodVq4eLiglOnTkEIgaKiIkRGRl7JXRMR0a/Q45b+K6+8guPHj6OxsRELFizAvffei/j4eGRkZKCgoAB6vR6pqakAgMDAQERHRyM1NRVqtRpJSUlQqztfV5KTk7Fhwwa0trYiPDwcERERtn1kRERkocem/+STT1odX7p0qdXxhIQEJCQkWIwHBQVJx/kTEdHVwTNyiYgUhE2fiEhB2PSJiBSETZ+ISEH69eQsIiL6t0sv0dB1Upcm+x9Xp5j/j1v6REQKwqZPRKQgbPpERArCpk9EpCBs+kRECsKmT0SkIGz6REQKwqZPRKQgbPpERArCpk9EpCBs+kRECsJr7xARyYC16/QA/X+tHm7pExEpCJs+EZGCsOkTESkImz4RkYKw6RMRKQibPhGRgrDpExEpCJs+EZGCsOkTESkImz4RkYKw6RMRKQibPhGRgvCCa0REdubXXJyNW/pERArCpk9EpCBs+kRECjLg+/RLS0uxZcsWmEwmTJ48GfHx8QNdAhGRYg3olr7JZMLmzZvx3HPPISMjA/v27UNFRcVAlkBEpGgD2vTLysrg5+eHIUOGwMHBATExMSgpKRnIEoiIFE0lhBADdWdfffUVSktLsWDBAgBAUVERvvvuOyQlJZnl8vLykJeXBwBYs2bNQJVHRHTNG9AtfWuvLyqVymIsLi4Oa9as6bbhL168uNf32ZesLee2t6xc6pBDVi512FtWLnXIISunOga06Xt7e6Ourk66XVdXB61WO5AlEBEp2oA2/aCgIFRWVqK6uhrt7e0oLi5GZGTkQJZARKRomuXLly8fqDtTq9Xw8/PDa6+9hk8++QS33norbrnlliuaa9SoUTbJ2nJue8vKpQ45ZOVSh71l5VKHHLJyqWNAP8glIqKri2fkEhEpCJs+EZGCsOkTESkImz4RkYIM6NE7V6q0tBQfffQR8vLyUFxcjOPHj0Oj0cDPz6/Xc7z//vsYM2aMxbzHjx/H4MGDMXjwYGm8oKAAI0eONMsKIfDll1+ioqIC1113HY4dO4Zdu3ahuroao0aNsnqS2aVWrFiBiRMnWow3NDTAyclJul1UVIT8/HzU1dVh5MiRZvPu378fnp6eGDRoEBoaGpCdnY333nsPx48fR0hICFxdXaXs22+/DWdnZ+j1+h6fm6amJvzjH/9AZWUlRo4ciZycHPz973/H6dOnERQUhEGDBpnljx07Jq2PL7/8EqdPn4avry/c3Nyszn+1158c1h1gn+vPVuuua257WX+2WneA7f/+fkn2R++89dZbqKysxIQJE+Dt7Q2g86SuoqIi+Pn5ITExsVfzLFy4EK+//rp0e9u2bTh58iRGjhyJgwcP4o477sDUqVMBAGlpaXjppZfMln/zzTdRX1+P9vZ2uLi4oL29HWPHjsXhw4fh6elpVsfTTz9ttqwQApWVlfD39wcAvPzyy9LPLr2vDz74AN9++y3GjRuHQ4cOQafTYc6cOVI2JSUFGRkZAICMjAyMHj0a0dHROHr0KL744gv8+c9/lrJJSUnw8fFBQ0MDYmJiMG7cOIsXsi6rV69GYGAgmpub8dNPP2HYsGGIjo7GkSNHcPbsWSxatEjKvvPOO6ivr8dNN92EkpIS+Pr6YujQofj0008xffp0REdHm80th/Unh3UH2N/6s9W6A+xv/dlq3QG2/fuzRvZfl3j48GG8+uqrFuMxMTF44oknzFb47Nmzrc4hhEBra6vZ2MGDB7F27VpoNBrMnDkTmZmZqKqqwpw5c6xeLuLEiRNIT09He3s7fv/73+ONN96Ag4MDxo8fb7ZSAMDHxwcuLi645557MGjQIAghsGzZMqSlpVmtrcv+/fuxYsUKODs7Y/z48RZ5k8kk/fvcuXNISUkBAEycOBG7du0yy3p7e2PNmjWorKzEvn378Nprr8FkMmHcuHEYN26c9EcAAAaDAc8++yyEEFiwYAG63vyFhobimWeeMZv30KFDSE9PBwCMGzcOy5cvx6xZs3DLLbdg2bJlFr90clh/clh3gP2tP1utO8D+1p+t1h1g278/a2Tf9B0dHVFWVobg4GCz8e+//x6Ojo5mY66urli9ejW8vLws5lm4cKHZbZPJBI1GAwAYPHgw0tLSsGnTJqxfvx7t7e0Wy3dlHRwcEBQUBAcHB2lcrTb/aCQtLQ379+/HG2+8gbvuuguRkZHQaDTw8fGxmLe1tRWnT5+GEAImkwnOzs7S/fxy3htvvBHvvfcepk+fjhtvvBH79+/HzTffjGPHjpm9vQT+fU2joUOHYsaMGZgxYwbOnj2Lffv2YfXq1XjttdekrBACTU1NaGlpQUtLC6qrq+Hr64vGxkaL50KtVqOpqQlubm4wGo3SH4Obm5vVF0s5rD85rDvA/tafrdYdYH/rz1brDrDt3581sm/6f/jDH/Dmm2+iubnZ7C2mi4sLHn30UbNsbGwsamtrrf7ijRs3zuz2kCFDcPz4cWlfo1qtxsKFC7F9+3Z8/fXXFst7eXmhpaUFzs7OWLJkiTR+/vx56ZfwUjfffDN+85vf4L333kN+fr7VFxIA0Gq12Lp1KwBIK1Kr1aKxsVH6Ze8yd+5cfPjhh3jiiScAALt27YKTkxPGjh2Lxx57zCxr7Rdg+PDhGD58OB588EGz8fj4eGnLZeHChdi0aRMAoKKiAjNnzjTLTp8+HYsWLYK/vz9++uknzJs3D0Dn/tHhw4db3Kcc1p8c1h1gf+vPVusOsL/1Z6t1B9j2788a2e/T73L+/HkYDAYIIeDt7W31l6svut5y/vJDEqDz7ZZOp+vVPC0tLbh48SI8PT27zZw5cwanTp3Cbbfd1uv6TCYT2trazD5outSFCxfQ0dEBd3f3buvq2nLp7f0JIaDRaNDR0YEzZ85Ap9NZvSBeU1MTqqqq4OfnZ/YB3OXIcf1drXUH2Nf66+91B9j3+uvvddd1n7b8+zMj7ERbW5vFWH19/YBm5VKHvWU7OjpER0eHtNz3338vGhsbFZeVSx19rflSn3zySa9yfc3acm57y9p6btnv3jl27Bj+53/+B21tbRg5ciR+//vfw9fXFwCwatUqs0/5bZWVSx32lgU6PyDLzs6GSqXCvHnzkJOTAycnJ1RWViI5OdnsKqvXclYudfQl+89//hO/lJOTg7a2NgDAnXfe2W1WCIHc3Fyr2b7mf022v2q2VdbWc1sj+6b/zjvvYMmSJQgMDMRXX32FlStX4o9//CNCQkIs9p3ZKiuXOuwtC3Qeo71u3Tq0trbimWeewerVq+Hv74+amhqkp6ebNZlrOSuXOvqS3bFjByIiIhAYGCitW5PJhObmZov13JesLee2t6yt57ZG9k2/vb0dgYGBAIBbbrkFAQEBePnll/HQQw9ZnJRhq6xc6rC3bJeufcB6vV46XM3Hx8fqi8S1nJVLHb3Nrl+/Hm+//TZaWlowc+ZMODk5obCw0OLDxb5mbTm3vWVtPbc1sr8Mg0ajwfnz56XbgYGBWLp0KXbu3InKysoBycqlDnvLduk6rOzSQ/dMJpPVoyqu5axc6uhtVq/X46mnnsL111+PlStX4quvvrL6mPqateXc9pa19dzWyP4yDL6+vhBCmB0x4OrqinHjxsHR0RGhoaE2z8qlDnvLAsDIkSPh7u4OjUZjdkRGTU0N9Hq92WFm13JWLnX0tWYACAgIQExMDL744gu0tLRg0qRJFpkrydpybnvL2nruS9nNIZtERPTryX6f/oULF5CTk4OSkhI0NDQAADw9PREZGYn4+Hiz41RtlZVLHfaWlUsdcsjKpY4rzdbX10OlUvVL1pZz21vW1nNb1acDPK+ClStXipycHGE0GqUxo9EocnJyxPPPPz8gWbnUYW9ZudQhh6xc6pBDVi51yCFr67mtkf0HudXV1YiPjzfbh+zl5YX4+HjU1tYOSFYuddhbVi51yCErlzrkkJVLHXLI2npua2Tf9H18fPD3v//d7IiR8+fPIzc31+J61bbKyqUOe8vKpQ45ZOVShxyycqlDDllbz22N7D/IbWpqQm5uLg4cOID6+noAna9sY8eORXx8vNkXB9gqK5c67C0rlzrkkJVLHXLIyqUOOWRtPbdVvdoJdJVVVFSIb775RjQ3N5uNHz58eMCycqnD3rJyqUMOWbnUIYesXOqQQ9bWc/+S7I/T3717N7Zu3Yrq6mrs2LEDvr6+CAgIAND5DTZTpkyxeVYuddhbVi51yCErlzrkkJVLHXLI2npua2R/yGZ+fj5eeuklODs7o7q6GuvXr0dNTQ3uuOMOi9PGbZWVSx32lpVLHXLIyqUOOWTlUoccsrae2xrZN/1Lv9HG19cXy5cvR3p6OmpqaiwepK2ycqnD3rJyqUMOWbnUIYesXOqQQ9bWc1sj+6N3vLy8cObMGem2s7MzFi9ejMbGRvzwww8DkpVLHfaWlUsdcsjKpQ45ZOVShxyytp7bql7t+b+KamtrzU5EuNSJEycGJCuXOuwtK5c65JCVSx1yyMqlDjlkbT23NbI/ZJOIiPqP7HfvEBFR/2HTJyJSEDZ9IiIFYdMnApCZmYkNGzaYjR0/fhxz586F0Wi8SlUR9T82fSIAiYmJOHz4MI4cOQIAaG1txaZNm/DII49Aq9X22/10fVUh0dUi+5OziAaCu7s75s6di02bNiE9PR0ffvghhgwZgokTJ8JkMiE3Nxd79+7FhQsXEBYWhuTkZLi5ucFkMiEjIwPffvst2traMGLECCQnJ+O6664D0PkOwtXVFVVVVfj222+xePFi3HjjjVf50ZKS8ZBNokukp6ejvb0dJ0+exNq1a6HX6/GPf/wDJSUlSElJgZubGzZv3oz29nY89thjMJlMKCoqwu9+9ztoNBr89a9/xXfffYc1a9YA6Gz6hw8fxrPPPovg4GB0dHTA0dHxKj9KUjLu3iG6RFJSEo4dO4YZM2ZI1yfPy8vDAw88AJ1Oh0GDBmHmzJn48ssvYTKZoFarMXHiRLi4uEg/Ky8vR0tLizRnVFQUQkJCoFar2fDpquPuHaJLeHl5wcPDQ9o9AwC1tbV46aWXoFKppDGVSoWGhgZ4eHhg27Zt+Oqrr9DY2ChlGhsbpWuk9PbLLYgGAps+UQ+8vb3x+OOPY/To0RY/27t3Lw4fPoylS5fCx8cHjY2NSE5O7vXFr4gGGnfvEPVgypQpePfdd6XvIK2vr8eBAwcAAM3NzXBwcIC7uzsuXryI7du3X81SiXrELX2iHtx5550AgOeffx7nz5+Hp6cnxo0bh8jISEyaNAlHjhzB/Pnz4e7ujpkzZyIvL+8qV0zUPR69Q0SkINy9Q0SkIGz6REQKwqZPRKQgbPpERArCpk9EpCBs+kRECsKmT0SkIGz6REQKwqZPRKQg/w+D/4dRNPTSHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "y1_schedule = schedule1.set_index('Payment_Date').resample(\"A\")[\"Interest\"].sum().abs().reset_index()\n",
    "y1_schedule[\"Year\"] = y1_schedule[\"Payment_Date\"].dt.year\n",
    "y1_schedule.plot(kind=\"bar\", x=\"Year\", y=\"Interest\", ax=ax, label=\"30 Years @ 5%\")\n",
    "\n",
    "plt.title(\"Interest Payments\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
